Running Machine Learning Systems in Production
Machine learning engineering is the practice of applying machine learning science to production systems. It requires expertise in both machine learning methods and software engineering. In practice, few individuals have sufficiently deep experience in both fields to act as sole practitioners. Scientists and engineers instead must work together, leveraging the skill and experience of one another, to build state-of-the-art machine learning enabled systems.
In this masterclass, Garrett Smith, founder of Chicago ML and creator of Guild AI, teaches the fundamentals of machine learning engineering. The class is tailored to both software engineers and data scientists.
The masterclass teaches how to:
- Validate problem definition and approach given available data and methods
- Select preliminary models
- Recreate results on benchmark data sets
- Develop training and validation data sets
- Automate experiment tracking and measurement
- Adapt models and tune performance
- Deploy and support production models
Students participate through hands-on exercises that follow best practices at each stage of development. They learn concepts as well as tools and techniques they can apply to build production ML systems with optimal performance.
If you're interested in the applied side of machine learning - how you can most effectively support development of ML enabled systems - this masterclass gives you a solid foundation in a single day of practical teaching.
This masterclass requires proficiency with the Python programming language and experience running programs in a command line environment. Knowledge of machine learning fundamentals is helpful but not required.
